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MEASURING THE GENEROSITY OF UNEMPLOYMENTBENEFIT SYSTEMS: EVIDENCE FROM HUNGARY

AND ELSEWHERE IN CENTRAL EUROPE

ELENA BARDASI, ANA LASAOSA,JOHN MICKLEWRIGHT and GYULA NAGY

BWP. 1999/8

September 1999

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Budapest Working Papers No.1999/8Labour Research Department, Institute of Economics, Hungarian Academy of SciencesDepartment of Human Resources, Budapest University of Economics

Measuring the Generosity of Unemployment Benefit Systems: Evidence from Hungaryand elsewhere in Central Europe

Authors: Elena BARDASI, CEPS/INSTEAD International Network for Studies inTechnology, Environment, Alternatives, Development, LuxembourgE-mail: elena@lissy.ceps.luAna LASAOSA, European University Institute, FlorenceE-mail: lasaosa@datacomn.iue.itJohn MICKLEWRIGHT, UNICEF International Child DevelopmentCentre, Florence E-mail: jmicklewright@mail.unicef-icdc.itGyula NAGY, Department of Human Resources, Budapest University ofEconomics E-mail: gynagy@sunshine.bke.hu

Published by the Institute of Economics, Hungarian Academy of Sciences.Budapest, 1999.

With financial support from the Hungarian Economic Foundationand the Foundation for Job Creation

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MEASURING THE GENEROSITY OF UNEMPLOYMENT BENEFIT SYSTEMS:EVIDENCE FROM HUNGARY AND ELSEWHERE IN CENTRAL EUROPE*

ELENA BARDASI, ANA LASAOSA, JOHN MICKLEWRIGHT and GYULA NAGY

The paper considers two aspects of the targeting of unemployment benefitsystems: (a) the probability that benefit is received in the population of thoseunemployed on standard international criteria of search and availability, and(b) the probability in the population of benefit recipients that search isconducted. The focus is on Hungary but stylised facts for a range of CentralEuropean countries and two EU comparators are derived in the first part of thepaper. The second part of the paper finds that most of the large decline incoverage of the Hungarian unemployed by insurance benefit (received by onlya quarter of the searching stock in 1997) cannot be explained by changes inthe composition of unemployment observable in labour force survey data(including unemployment duration). The probability of active search (searchother than through a state employment office) is found to be very similar forthose receiving insurance and assistance benefit.

1. INTRODUCTION

Descriptions of the generosity of unemployment benefit schemes typically

rely on two methods of presentation. The first, often found in cross-national

comparisons of benefit systems (e.g. Burda (1993), OECD (1994)), concentrates

on the benefit entitlement of hypothetical persons, for example someone who

previously earned the average industrial wage and who has a good employment

history. Calculations are made for different durations of unemployment and for

persons with different family circumstances, for example a married man with

dependent wife and two dependent children. For obvious reasons, this is an

unsatisfactory method of presentation since the assumed characteristics cannot

* The research for this paper was financed by EC PHARE-ACE grant P95-2081-R (“StateResponses to Poverty and Unemployment in Hungary”). We thank the HungarianCentral Statistical Office, and especially Judit Lakatos, for providing us with labour forcesurvey microdata.

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be representative of the wide range of types of person who actually experience

unemployment. Moreover, benefit systems often have rules that defy easy

summary through stylised calculations.

The second method focuses on benefit coverage, presenting proportions

of the registered unemployed receiving benefits of different types, whether across

countries or across time. Administrative sources accurately record how many

persons do actually receive benefit of different types, but are likely to give biased

estimates of benefit coverage of the genuinely unemployed – biases that will vary

over time and across country and between different sub-groups in the

unemployed population. Many persons who are unemployed may not register as

such, precisely on account of their lack of eligibility for unemployment benefits.

And, on the other hand, administrative data will contain persons receiving benefits

who are not genuinely unemployed.

This paper focuses on measurement of two basic aspects of benefit

coverage that represent sides of the same coin: (a) the proportion of all persons

defined as unemployed on the standard ILO/OECD criteria of search and

availability who do in fact receive unemployment benefit, and (b) the proportion of

all persons who receive benefits who do in fact conduct search. Although

coverage is not usually analysed in this way, these are two of the natural

yardsticks for assessing the success of income support policy. Do the genuinely

unemployed receive help from the state? And are those receiving help from the

state genuinely unemployed? The recent renaming in the UK of unemployment

benefit as “Job Seekers’ Allowance” emphasises this way of looking at benefit

systems – benefit is intended for those who search (although it is clear that

search is seen as a necessary and not a sufficient condition). One might

characterise a well-targeted unemployment benefit system as being generous in

coverage towards searchers but restrictive in coverage of those not searching.

The labour force survey data we use in this paper allow analysis of both these

aspects of benefit generosity.

In the first part of the paper we derive some stylised facts about benefit

targeting, in the sense just given, in five Central European countries – the Czech

Republic, Hungary, Poland, Slovakia and Slovenia – and two Western European

countries – Spain and the UK. There has been a lively debate on the issues

surrounding unemployment benefit in Central Europe during transition (e.g.

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OECD, 1996), but no analysis that we are aware of has considered the overlap

between the populations of benefit recipients and searchers in different countries.

Spain and the UK are countries where the association between benefit and

search has seen much more analysis (although not in the way we approach the

issues) and they present two differing compactors for the Central European

group. The results show that less than half of the unemployed in Slovenia,

Poland, and the Czech Republic received any form of unemployment benefit in

1994-95, a characteristic shared by Spain, but that among this group only in

Slovenia did less than half of those with benefit report search. In all the countries

analysed, benefit is less well-targeted on women: fewer searchers receive benefit

than men and fewer women with benefit undertake search, although the gender

differences are notably smaller in the Central European countries than in the two

Western compactors.

In the second part of the paper we investigate why targeting of benefit has

clearly worsened over the 1990s in one of the Central European countries,

Hungary. Both the proportion of ILO/OECD unemployed with unemployment

benefit and the proportion of those with benefit who searched fell in Hungary by

some 15 percent points over 1993-97. In particular, the coverage of

unemployment insurance secured by prior contributions fell enormously and now

provides support to only a quarter of the unemployed seeking work. To what

extent was this due to change in the composition of the unemployed, as opposed

to cuts in the generosity of insurance benefit? We find that the rise in long-term

unemployment and the changing composition of the unemployed in terms of other

observable characteristics as age and education do not explain most of the fall in

benefit coverage, which must therefore reflect a combination of the impact of

policy changes and worsening work histories. We find no evidence that the

frequency of active search differs between those receiving insurance benefit and

those receiving the means-tested assistance benefit that has grown in

importance, a result that calls into question the view that those on assistance

benefit are less attached to the labour force.

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2. CROSS-NATIONAL COMPARISONS OF BENEFIT COVERAGE: CENTRALEUROPE

To compare coverage of unemployment benefit systems across countries

we draw on the labour force survey microdata in the Luxembourg Employment

Study (LES). This is the recently developed twin sister of the well-known

Luxembourg Income Study (LIS) that brings together and harmonises income

surveys from a range of countries. The surveys in the LIS are often of a very

different nature, while the LES has the advantage that its constituent labour force

surveys are more inherently comparable, with common definitions of some

important concepts. These include the definition of unemployment, which follows

the ILO/OECD definition of search and availability. Hence using the LES we are

able to see how two key aspects of benefit targeting – coverage of searchers and

search by recipients –varies across a group of countries. Our principal interest is

in the five Central European countries that were included in the LES from its

conception, but we also take the UK and Spain as Western European

compactors.

As with all cross-country comparisons, results may be sensitive to the year

taken for analysis and this is a particular concern in the study of transition

economies where labour market developments and policy changes may quickly

lead to changes in the picture obtained. However, in contrast to the LIS, the LES

does not currently provide any choice of year for analysis. (The second half of

the paper shows the changing picture for Hungary with data obtained directly from

the Hungarian Central Statistical Office.) The Central European data in the LES

relate to 1993 for Hungary and 1994 for the Czech Republic, Poland, Slovakia,

and 1995 for Slovenia. The UK data refer to 1989 and those for Spain to 1993.

Figure 1 shows the unemployment rates in these years for the quarter for which

survey data are included in the LES, distinguishing between the measure using

the ILO/OECD definition taken from the survey data and that based on the official

unemployment register.

At just under 4 percent, the Czech Republic has much the lowest

unemployment rate in the Central European group, less than half that in the

country with the next lowest rate, Slovenia. Rates in Hungary, Poland and

Slovakia are all around 13-14 percent. The two EU compactors have rates that

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differ sharply, with the UK coming between the Czech Republic and Slovenia

while Spain far outstrips all the Central European group with over a fifth of the

workforce unemployed. To what extent are these differences in unemployment

rates associated with a very different proportion of long-term unemployment –

those who have searched for more than a year? The long-term unemployed are

less likely to receive insurance benefit limited by duration of receipt, and

variations in the degree of long-term unemployment may be one important cause

of differences in benefit coverage. Figure 1 shows that while the Czech Republic

in 1994 had a notably low share of long-term unemployment to go with its

markedly low unemployment rate, in general there is no simple association

between the two measures, as the comparison of Slovenia and Spain or Hungary

and Slovakia highlights.1

Figure 2 shows the basic picture of benefit coverage in the labour force

surveys contained in the LES data. The vertical axis shows the proportion of the

ILO/OECD unemployed that receive unemployment benefit while the horizontal

axis shows the proportion with unemployment benefit that report having searched

in the last month. In our terms, “good” targeting is associated with high values on

both measures. “Unemployment benefit” includes both contributory

unemployment insurance (UI) and means-tested “unemployment assistance” (UA)

or its equivalent (any means-tested benefit paid to the unemployed that requires

registration of unemployment status as a condition for eligibility).2 Although UI

and UA are often combined in this way in analyses of unemployment benefit, the

two are different in nature, both as regards the income support they provide and

1 The sample sizes of ILO/OECD unemployed vary substantially from country to countryon account of survey size as well as unemployment rate. The unemployment rates inFigure 1 are based on the following numbers of individuals classified as unemployed:Czech Republic, 1171; Hungary, 3554; Poland, 4189; Slovakia, 2007; Slovenia, 1023;Spain, 15751; UK, 5388.2 This means that some means-tested benefits that the unemployed may receive onaccount of their low incomes alone, as opposed to a combination of low income andregistration as unemployed, are not included. In the case of Poland this excludes onesignificant source of means-tested income support for the long-term unemployed; Goraand Schmidt (1998) report that 1 in 5 persons exhausting UI go on to receive mean-tested social assistance, a benefit that does not require registration as unemployed.However, if we wish to analyse “unemployment benefit” it seems appropriate to restrictattention to those forms of income support in which registration of unemployment is anecessary condition for benefit. (Hence universal or means-tested family benefits, whichmay provide important income support to unemployed households with children, areexcluded.)

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the effects on behaviour that can be expected (Atkinson and Micklewright, 1991).

UI provides an incentive for formal employment: to maintain an insurance record

to draw down on if need be at a later date. Means-tested UA constitutes a

disincentive to the labour supply of other household members. UI typically

provides a higher level of income support than UA (of course this may mean a

greater disincentive to return to work). We distinguish between the two types of

benefit later in the section.

The diagram includes separate results for men and women. The first

obvious feature is that in each country the targeting of benefit is unambiguously

worse for women – the data-point for women lies to the south-west of that for men

in every case. Unemployed women are less likely to get benefit than men and

among those with benefit they are less likely to be searching. However, the

gender differences are notably greater for the two Western European countries.

Twice as many men in the UK receive benefit as do women and the same is true

in Spain although the absolute levels of receipt here are much lower. And in both

countries the proportion of women with benefit who search is about 15 percent

points less than for men. By contrast, the gender differences in the Central

European countries are typically small. For example, the proportion of the

unemployed getting benefit is very similar for the two sexes in Slovenia and the

Czech Republic.

The second clear feature of the diagram is that only two countries are

found in the north-east quadrant for both sexes, where targeting can be

considered best; only in Hungary and Slovakia is it the case that both the majority

of unemployed men and of unemployed women get benefit and that the majority

of both sexes with benefit did search in the last month. The UK is in this quadrant

for men but low benefit coverage pulls it down into the south-east quadrant in the

case of women. Slovenia is the only country in the south-west quadrant where

both coverage and search frequency are less than 50 percent. Viewed in this

way, the Slovenian benefit system appears the least well targeted. Benefit

coverage in Slovenia is similar to that in Poland and the Czech Republic (and also

to that for Spanish men) – it is the low search propensity that singles out

Slovenia. Overall, the levels of benefit coverage in these three Central European

countries may come as some surprise – less than half the unemployed are

receiving benefit.

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One reason for the differences across country are the variation in the

proportions of unemployed who have never worked or who have worked only a

long time in the past, something associated with a low probability of coverage by

insurance benefit. Figure 3 shows how the picture of targeting changes when we

exclude those who have never worked or who last worked more than two years

ago. In the case of the benefit recipients, the diagram also excludes those

classified as employed on the ILO/OECD criteria. Questions on search are not

asked of these persons although some may in fact be legally receiving benefit

while undertaking part-time work (as is allowed in Hungary for example).

The effect for most countries is to improve targeting, i.e. to move them

towards the north-east. Slovenia and Poland now join Hungary and Slovakia in

the north-east quadrant and women in the UK almost make it there to join their

male counterparts. In five countries – the UK, Slovakia, Poland, Spain and the

Czech Republic – the percentage of men with benefit who search is almost 90

percent or more and among men it is only Slovenia which appears much of an

outlier.

These results refer to those persons performing any search and a key

point here is that it includes persons whose only search in the previous month

was a visit to the employment office. The questions on search in labour force

survey questionnaires are structured in a way that they come separately from

those on receipt of unemployment benefit. Respondents are asked whether they

have searched in the last month and then about what methods of search were

used, of which visiting the employment office is one. Nevertheless, the suspicion

must remain that some of those reporting only that they searched via a visit to the

employment office were in fact there primarily for their regular registration in order

to collect unemployment benefit. This leads us to distinguish between “active”

and “passive” search. We define active search as the use of any method other

than visiting the employment office, which we label as being a passive method.

The active methods are varied in nature and some (e.g. visiting employers) are

clearly more active than others (e.g. checking newspaper advertisements).

Nevertheless, this distinction serves to isolate those whose only action is to visit

the state employment office, which they may do in any case to draw benefit.

It is active search that policy makers are typically most concerned to see

occur. Search through public employment offices is often viewed as being rather

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ineffective. Benefit regulations in the UK require the unemployed receiving

benefit to maintain a diary of their search activity that they may be called on to

produce at any time. Such activity is presumed to include more than visiting the

employment office.

Figure 4 shows the benefit targeting of active searchers – the proportion of

those actively searching who get benefit and the proportion of those with benefit

who actively search. In both cases the active search may be in conjunction with

passive search – we are identifying those conducting any active search with or

without passive search. However, in the case of the Czech Republic the coding

of search methods is such that active search can be identified only if no passive

search is conducted and for this reason this country is excluded from Figure 4.

Compared to Figure 3, the data-points shift to the left. The direction of

change, of course, is clear a priori since a proportion of benefit recipients shown

as searching in Figure 3 are only conducting passive search. The extent of this

shift is not clear a priori and is large in several cases. Slovakia moves from being

the country with the highest search frequency among benefit recipients in Figure 3

to being the lowest in Figure 4: only a quarter of benefit recipients conduct active

search compared to around 90 percent who perform any form of search. Spain

also shifts a long way to the left, while other countries experience only modest

moves. In the case of Slovakia there must be some suspicion that the method of

coding hides some active search; in this country only one search method is coded

with the combination of active and passive search being one of the coded values.

However, the practice followed in Spain of coding up to three separate search

methods follows that in the other countries so no such possible explanation could

apply here. The UK has the smallest move of all, implying that the great majority

of benefit recipients who search are undertaking some form of active search.

By contrast with these horizontal shifts, the proportions of searchers

receiving benefit – the figures on the vertical axis – change little from Figure 3.

This implies that the proportion of the ILO/OECD unemployed who receive benefit

is a good guide to the proportion of active searchers who are in receipt.

How does benefit receipt vary over duration of unemployment? One might

assume that the shorter the duration of unemployment, the more likely the

individual is to be receiving benefit, since UI entitlement will be less likely to have

expired. This is certainly an assumption behind much discussion of

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unemployment benefit and the labour market. Figure 5, which restricted to the

same sample as Figure 3, sheds light on the matter and provides some

interesting results. Among men, the very short-term unemployed – 0-3 months

since last worked – are less likely to be in receipt of benefit that any other

duration group, including those unemployed for over a year. This is true in all

countries except Poland. The incidence of benefit is typically 10-20 percent

points lower at 0-3 months than at 4-6 months. A number of reasons may apply

here including delays in claiming benefit at the start of the spell, and delays in

eligibility due to a voluntary quit or severance pay.

The 4-6 month duration is where coverage for men is highest in 4

countries, the Czech Republic, Poland, Slovenia and Spain. In Hungary benefit

coverage is highest in the 7-12 month group while in Slovakia and the UK it is

highest for those unemployed over a year (those out of work for more than two

years are excluded from the diagram). The results for women are similar

although the lack of variation with duration in Spain and the UK now become even

more pronounced.

To this point we have not distinguished between receipt of different types

of unemployment benefit. But the distinction between UI and UA is an important

one, as noted earlier. Table 1 shows how the type of benefit received by those

working in the last 2 years. Spain is excluded from the table since the data for

this country do not distinguish UI and UA. Poland is also excluded since at the

time of the data collected that are present in the LES there was no UA.3 The

variation in the coverage of insurance benefit is striking. Whereas only 15

percent of the male ILO/OECD unemployed are shown receiving UI in the Czech

Republic and in Slovakia, the figure is as high as 70 percent in Hungary (the

figures for women are very similar).

How does Hungary emerge in general from these international

comparisons? The lower coverage of women by benefits and the lower frequency

of search by women receiving benefits is a feature shared with all the other

countries included. The coverage of insurance benefit seems notably high in

1993, although as Section 4 makes clear this has since declined very sharply.

3 See footnote 2 for further information on means-tested benefit in Poland.

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3. DECOMPOSING DIFFERENCES IN BENEFIT COVERAGE

In this section we consider how to investigate further one of the two key

aspects of targeting, the proportion of searching unemployed who receive benefit.

How should one try to isolate the effects of the various factors that change the

coverage of unemployment benefit from country to country or over time? We

observe that benefit coverage changes over time or across space but this could

be due to differences in the composition of the unemployed (in terms of

entitlement or claim behaviour) or to differences in the rules determining eligibility

– or to some combination of the two.

The ideal approach would involve simulation with a benefit prediction

model that incorporates differences in the rules determining eligibility. In this way

we could – to take the case of changes over time – simulate coverage in year t

using the rules applying in year t+d and hence accurately decompose the change

in observed coverage between the two years into that part due to a change in

rules and that part due to a change in the characteristics of the unemployed. Or,

if the rules are fixed over time, changes in coverage could be decomposed into

those arising from characteristics of the unemployed and those from claim

behaviour. This is the approach adopted by Blank and Card (1991) in their

analysis of changing coverage of insurance benefit in the USA.4

Unfortunately, labour force survey data do not contain sufficient detail for

us to simulate benefit entitlement. We therefore adopt a different approach,

attempting to decompose changes in coverage using multivariate analysis.

Simplifying matters somewhat, the probability that an individual receives

unemployment benefit can be written as a function of his or her work history and

unemployment duration, which determines UI entitlement, and household income,

which determines entitlement to UA.

p(benefit) = f (work history, duration, household income) (1)

Most aspects of work history and household income are unobserved in the data to

hand, but we observe various individual and household characteristics that

4 See also Creedy and Disney (1981) who simulate entitlements on assumed populationsof the unemployed.

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influence them, including the age and education of the claimant and the number

and type (including employment status) of other persons in the household.

work history = g1(observable characteristics) (2)

household income = g2(observable characteristics) (3)

Hence we can estimate a model explaining the probability of benefit as a result of

these characteristics and duration:

p(benefit) = h (observable characteristics, duration) (4)

The function h captures both function f and functions g1 and g2.

Equation (4) could be estimated on data from different countries, as in the

labour force survey data from the LES, or on data from one country drawn from

different years. What would the estimated coefficients reveal? They will show

the impact in country j or year t of observable individual and household

characteristics and time spent unemployed on the probability of receiving benefit.

These estimates will vary across country or across time either because the rules

determining benefit differ (the function f) or because the relationship between

observable characteristics and unobservable work histories and household

incomes differ (the functions g1 and g2).

It is obvious that rules determining the impact of work history and

household income vary substantially across countries; although they share some

common general form, UI and UA schemes display considerable country-specific

features. But it is also the case that the relationship transforming characteristics

observable in labour force surveys into unobservable work histories and

household incomes also vary from country to country. A young person of low

education is likely to have a poorer work history than average in all countries but

the extent to which this is the case is unlikely to be the same everywhere.

Married women with young children are much more likely to have worked in the

past in some countries than in others.

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The differences in the functions g1 and g2 may be less over time within a

given country but nevertheless can certainly be expected to occur. A particular

constellation of observable characteristics in one year may be associated with a

work history that is different to that several years later, especially when the nature

of the labour market is changing, as in the transition countries of Central Europe.

At the start of the transition there was little or no open unemployment and full

employment had prevailed more or less for many years in most countries.

Therefore differences in recent work histories across levels of education or

between age groups were relatively small compared to the differences that can be

expected in the mid and late 1990s given the concentration of unemployment on

particular groups in the intervening years.

In what follows we estimate equation (4) by probit for Hungary alone for a

series of years. We estimate equations using data from year t+d and then use the

results to predict entitlement for individuals observed in year t. The mean

predicted probability in year t using the year t+d coefficients will give an unbiased

estimate of the coverage of benefit in year t were individuals to have received

benefit determined as in year t+d.5 In this way we can identify the extent to which

changes in benefit coverage over time are explained by changes in observed

characteristics, including the duration of unemployment. In particular this allows

us to conclude whether the rise in long-term unemployment in Hungary has had

much impact on benefit coverage.

4. BENEFIT TARGETING IN HUNGARY

In this section we investigate the changes that have taken place in benefit

targeting in Hungary in the 1990s, using in part the framework described in the

previous section. Figures 2 to 4 showed how targeting in Hungary in 1993

compared to that in other countries but it was noted that the picture could change

over time. Here we address this issue directly.

The two parts of Figure 6 show how targeting of benefit changed between

1992 and 1997, distinguishing between men and women. Unemployment in

Hungary on the ILO/OECD measure peaked in the 1990s in the first quarter of

1993 – the quarter of data in the LES database used in Section 2. Average

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unemployment in 1993 was 12 percent and it has since fallen reasonably steadily

to 9 percent in 1997. The top half of Figure 6 focuses on all ILO/OECD

unemployment and does not distinguish the two main types of benefit, UI and UA.

Targeting appears to get unambiguously worse over the period, in that there is a

move in the diagram towards the south-west. Whereas in 1992 some three-

quarters of men with benefit were conducting search and over 60 percent of men

who were unemployed on the ILO/OECD criteria received benefit, by 1997 these

figures had fallen to 57 percent and 48 percent.6 In other words, not much more

that half of men with benefit were searching and less than half of unemployed

men were in receipt of any benefit. Similar falls are registered for women.

The bottom half of the diagram shows the changes when we restrict both

axes to active search (with or without passive search). As already shown in the

cross-national comparisons, the proportion of active searchers with benefit in

Hungary in 1993 is not much different to that of all searchers and we see that

again here in the data over time. Benefit coverage of active searchers falls with

that of all searchers and by 1997 only 45 percent of active searchers of either sex

receive any unemployment benefit.

By contrast, the proportion of benefit recipients who perform active search

does not appear to fall – the general direction of the move in the bottom part of

the diagram is towards the south rather than the south-west and in fact the figure

for women is higher in 1997 than in 1992. Put another way, the leftward shift that

is found in the proportion of benefit recipients who search when one moves from

considering any search to active search gets less over time. Whereas in 1997,

around 75 percent of male benefit recipients performed any search and 50

percent active search, by 1997 this gap had shrunk from this gap of 25 points to

only 8 points.

The key change in the 1990s is therefore the reduction in benefit coverage

of searchers and we now investigate that in more detail using the method outlined

in the previous section.

Figure 6 conceals the fact that the fall in benefit coverage has been

associated with a collapse in UI and a rise in the importance of UA. This is shown

5 This follows the growth accounting technique derived in Gomulka and Stern (1990).6 The figures for 1993 differ from those for Hungary in Figure 2 since they refer to thewhole year (in a period when coverage was declining) rather than to a single quarter.

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in Figure 7. Coverage by UI of the ILO/OECD unemployed falls from 60 percent

in 1992, when it was the only benefit available, to only 23 percent in 1996 before

recovering slightly to 25 percent in 1997. Almost identical coverage figures are

found for men and women in 1997 – only a quarter of the ILO/OECD unemployed

now receive insurance benefit. Figure 6 shows the rise in means-tested UA,

which by 1997 had become as important a form of income support as UI.

The discussion in Section 2 indicated why a switch in coverage from UI to

UA should not be a matter of indifference. The relationship between UI and UA in

Hungary follows the general characteristics of these two benefits in other

countries: UA paid at a flat-rate amount provides substantially less income

support to the majority of claimants than does earnings-related UI; it contains a

very crude means-test, and it is administered by what are often very small local

governments in a way that may both at times be somewhat arbitrary and that

provides little monitoring of search (Micklewright and Nagy, 1997, 1998).

Besides coverage of benefit, Figure 7 also shows the marked rise in long-

term unemployment in Hungary (persons searching for more than a year) from 15

percent in 1992 to 46 percent in 1996, something associated with the low turnover

of the unemployed stock in Hungary as in other Central European countries

(Boeri, 1994). Since UI is of limited duration, one’s first thought might be that it is

the lengthening durations of the unemployed that have been mainly to blame for

the collapse in UI coverage. However, the story is more complicated than this. It

is also the case that the 1990s have seen a reduction in entitlement periods for

UI. For successful claims to UI starting before the end of 1992, eligibility was for

between a minimum of 4.5 months and a maximum of 18 months, the former

brought about by one year’s insured employment in the last four years and the

latter by a continuous work history within this period, with entitlement varying

between the two extremes for intermediate work histories. For new claims from

1993, entitlement periods were cut by one third, hence to a total of between 3 and

12 months.

Using the framework in Section 3, we now try to shed light on the

importance of lengthening unemployment durations (and changes in other

observed characteristics) in explaining changes in the coverage of UI and UA.

For each gender and for each year, we estimate equation (4) by probit for UI and

UA separately for all ILO/OECD unemployed. The two benefits are mutually

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exclusive – UA can only be received by someone who has exhausted UI

entitlement in the current or a previous spell. Following the discussion of Section

3, the equations contain explanatory variables for age, duration of unemployment,

household status (numbers and types of other household members), and region.

The results for men for 1993 and 1997 are given in Table 2.

We go on to predict the probability of receipt of the two benefits for the

1993 sample using the coefficients of the other years and then calculate the

mean probabilities of receipt. This leads to the “predicted” frequencies of receipt

that are given alongside the actual sample frequencies for each year in Table 3.7

The “predicted” frequencies in Table 3 show receipt holding constant observable

characteristics at their 1993 values. As discussed in section 3, the probit

coefficients for each year summarise the rules determining entitlement – the

function f – and the links between the unobserved work histories and household

incomes on the one hand and observed individual and household characteristics

on the other – the functions g1 and g2. As argued in section 3, the latter can be

expected to have changed as well as the former.

The results in Table 2 show some clear patterns together with others that

are less easy to interpret. The impact of education on the probability of UI is

clearest for women in 1997. The base group is completed primary school and

persons with levels of education above this are significantly more likely to get

benefit while those who did not complete primary school are less likely. There

appears to have been a clear increase in the link between education and UI

between 1993 and 1997, which we interpret as reflecting the development of

poorer work histories during the intervening years for those with less education.

The results for men follow a similar pattern although less obviously. We give the

same interpretation to the big change in the coefficient on the dummy variable

indicating residence in one of the three high unemployment regions. By 1993,

residence in one of these regions indicates a weaker employment history or prior

receipt of UI (which uses up entitlement). Duration of employment clearly has a

change in impact between the two years (as one would expect from changing

7 The samples used in estimation are all those from the first quarter of each year plusthose new entrants to the survey found in the stock in later quarters. The labour forcesurvey has a quarterly rotating panel design with 5/6ths of the sample remaining in the

18

rules of entitlement). At durations of under a year the variation of benefit with

duration becomes notably less marked for both sexes. Many of the household

circumstance variables have little or no impact, except it is clear that other

employed persons in the household reduces the probability of receiving UA, an

effect that operates through the household means-test.

The results in Table 3 show clearly that changes in the composition of

observed characteristics of the unemployed, including time since last worked, do

not explain most of the fall in the coverage of UI or the rise in coverage of UA.

Holding observed characteristics constant at their 1993 levels, UI coverage for

men falls from 60 percent in this year to 31 percent in 1997, compared to the

actual fall to 25 percent. Similarly, predicted coverage for women falls from 57

percent to 30 percent compared to an actual fall to 26 percent. Predicted UA

coverage in 1997 for men is 23 percent and 24 percent for women, compared to

actual coverage of 25 percent and 23 percent respectively, up from figures of only

6 and 4 percent in 1993. The conclusion is therefore that the big changes in

benefit coverage in Hungary in the 1990s have had relatively little to do with the

changing composition of the unemployed in terms of duration of unemployment,

age, education or household composition.

The relative importance of changes in rules of entitlement and changes in

the work histories and incomes that generate entitlement is, however, impossible

to tell from the results. Existing claims to UI in progress in January 1993 were

“grandfathered” under the old entitlement rules and therefore the impact of the

introduction of the new rules on coverage in the stock was far from sudden.

survey each quarter. The “actual” figures for each year therefore differ somewhat fromthose in Figure 6 which are an average of the separate figures for each full quarter.

19

Search propensity

We have already noted that the change over time in the proportion of

benefit recipients that undertake active search is quite small. One important

policy issue, however, relates to any differences in the frequency of search

between those receiving UI and UA. UA is administered (and part-funded) in

Hungary by local governments, many of which are far too small to have any real

capability to monitor search behaviour of claimants. (Local governments have an

average population of only 3,000 people.) UA recipients must retain their

registration of unemployment at the local unemployment office but beyond this

minimal requirement there appears in many cases to be very little contact

between any organ of central or local government and the claimant after the

outcome of an application for benefit has been determined (Micklewright and

Nagy, 1996, 1997). One of the concerns expressed by policy makers in Hungary

is that, as a result, UA represents “easy money” for the unemployed.

The unconditional probability of active search does vary between the two

types of benefit recipient but the differences are not great. In 1997, the

proportions of UI and UA recipients conducting active search were 53 percent and

46 percent for men and 47 percent and 43 percent for women (see Figure 6).

Active search is thus somewhat more common for those on UI. These figures are

averages of separate quarterly data; when we use the same microdata samples

of benefit recipients as enter the estimation of the models in Table 2, we find that

differences in the probability of active search between the two groups of

recipients that are insignificant.8

Table 4 shows the results of probit equations of the probabilities of active

search for the two types of recipient, estimated with 1997 data when the UI and

UA were broadly of equal importance in terms of benefit coverage. Likelihood

ratio tests of these separate models against a pooled model with both benefit

types taken together result in failure to reject the pooled models, confirming that

the differences between search propensity of individuals on the two types of

benefit are not great. Nevertheless, some differences emerge and the result also

reveal some patterns of active search that are common to the two sexes.

8 See footnote 7 on the selection of the sample in Table 2.

20

Among men, all education levels beyond primary (the base) are associated

with more search, with the exception, surprisingly, of higher education (the

sample numbers are small in this case). The differences by education group are

less evident for women. UI recipients aged 50 or over of both sexes are less

likely to search but the same is not true of those of this age on UA. This may be

because many persons on UI within 5 years of retirement age leave UI for an

early retirement scheme and hence do not search while on UI. But conditional on

their not entering this scheme and then moving onto UA, their age has no impact

on their search activity. The search probability clearly varies with duration for

men, first rising and then falling, although the effects are not very well determined.

The results for women, however, do not really display this pattern. The

household circumstance variables have little impact for either sex and for either

benefit, which comes as some surprise. Perhaps the most notable results are the

lack of much impact of the presence of young children for women (the dummy for

a child aged 0-3 is only significant at the 10 percent for women) and the negative

impact (significant at the 5 percent level) of employed people in the household on

active search by women receiving UA, but not those receiving UA.

5. CONCLUSIONS

In this paper we have proposed two measures to be used when

considering coverage of unemployment benefit systems. These are linked to the

measurement of job search behaviour – the percentage of those without work

who search who receive unemployment benefits and the percentage of those with

unemployment benefits who do undertake search. Using these we have

generated some stylised facts about benefit coverage in Central Europe in the

1990s, taking the UK and Spain as EU compactors. And we have investigated

changing coverage in Hungary, proposing a framework for doing so that could

also be used in cross-national comparisons so as to control for variation in the

observed composition of the unemployed. Our principal findings include:

• Less than half of the searching unemployed in 3 of the 5 Central European

countries observed in 1993-95 received unemployment benefit. In the case of

men, this is a much lower figure than for the UK in 1989 but is similar to that

21

found for Spanish men in 1993. Coverage was higher in Hungary and

Slovakia.

• The proportion of benefit recipients recording search in the Central European

countries exceeded half in all cases, except Slovenia and is comparable to

that in the UK and Spain.

• The targeting of benefit on women is worse than that for men in all countries

considered; fewer searching women receive benefit and fewer women with

benefit do search. The differences were particularly marked in the two EU

compactors.

• The proportion of those actively searching who receive benefits appears in

general to differ little from the percentage of all searchers receiving benefits

(including those undertaking only passive search, defined as visiting public

employment offices).

• Among those receiving benefits, the coverage of contributory UI and means-

tested UA varied quite sharply. The coverage of UI was already very low in

the Czech Republic and Slovakia in 1994-95 – only 15 percent of searchers in

receipt – well below the level observed in the UK.

• Benefit coverage of searchers in Hungary fell to around 50 percent by 1996-97

from around 60 percent in 1992. The proportion of those with benefit who

performed any search also fell, but the propensity to search actively did not.

• Coverage by UI of searchers in Hungary by fell by some 35 percent points

over 1992-97. Changes in observed characteristics of the unemployed

(including duration of unemployment as well as age and education) appear to

have relatively little to do with this.

• The probability of active search appears to be similar for those receiving UI

and UA in Hungary in 1997, which calls into doubt arguments that the

transition from one benefit type to another is associated with a drop in search

activity.

22

REFERENCES

Atkinson A B and Micklewright J (1991) “Unemployment Compensation and labourMarket Transitions: A Critical Review”, Journal of Economic Literature, vol 29, pp1679-1727

Blank, R and Card, D (1991) “Recent Trends in Insured and Uninsured Unemployment;Is There an Explanation?”, Quarterly Journal of Economics, November, pp1157-1189

Boeri, T, (1994) “’Transitional’ Unemployment”, Economics of Transition, 2(1), pp1-26

Burda, M, (1993) “Unemployment, Labor markets and Structural Change in EasternEurope”, Economic Policy, vol 16, pp101-137

Creedy J and Disney R (1981) “Eligibility for Unemployment Benefits” Oxford EconomicPapers, vol 33, pp256-273

Gomulka J and Stern N (1990) “The Employment of Married Women in the UK, 1970-83”,Economica, vol 57, pp171-199.

Gora M and Schmidt C (1998) “Long Term Unemployment, Unemployment Benefits andSocial Assistance: The Polish Experience”, Empirical Economics, vol 23, pp55-85

Micklewright J and Nagy Gy (1994) “How Does the Hungarian Unemployment InsuranceSystem Really Work?”, Economics of Transition, 2(2), pp209-232

Micklewright J and Nagy Gy (1997) “The Implications of Exhausting UnemploymentInsurance Entitlement in Hungary”, Innocenti Occasional Paper 53, UNICEF InternationalChild Development Centre, Florence.

Micklewright J and Nagy Gy (1998) “Unemployment Assistance in Hungary”, EmpiricalEconomics, vol 23, pp155-175

OECD (1994) The OECD Jobs Study: Evidence and Explanations, OECD, Paris.

OECD (1996) Lessons From Labour Market Policies in the Transition Countries, OECDProceedings, Centre for Co-operation with the Economies in Transition, Paris.

23

Table 1: Receipt of Unemployment Insurance and UnemploymentAssistance benefits among the ILO/OECD unemployed

Czech Rp. Hungary Slovakia Slovenia UK% men with benefit: Insurance (UI) 15.0 70.1 15.8 45.7 55.6Assistance (UA) 23.6 3.4 46.6 12.1 19.3% women with benefit:Insurance (UI) 17.3 70.1 15.2 41.9 32.9Assistance (UA) 20.3 2.2 38.2 17.0 9.9

24

Table 2: Probit estimates of UI and UA receipt among the ILO/OECDunemployed in Hungary

a) Men

Coef. z Coef. z Coef. z Coef. zEducational levelincomplete primary 0.083 0.67 -0.286 -1.18 0.246 1.49 0.377 2.12vocational 0.172 2.98 0.133 1.63 -0.335 -3.26 -0.210 -2.72general secondary 0.006 0.05 0.244 1.71 -0.189 -0.82 -0.361 -2.26vocational secondary 0.045 0.52 0.209 1.65 -0.136 -0.86 -0.358 -2.67higher -0.220 -1.45 0.073 0.29 0.061 0.22 -0.612 -2.09Age-group (years)-19 -0.459 -4.05 -0.574 -3.08 0.047 0.21 -0.799 -3.7720-29 -0.103 -1.42 0.066 0.62 0.113 0.98 -0.260 -2.5840-49 -0.007 -0.09 0.203 1.95 -0.189 -1.47 0.019 0.2050-59 0.244 2.25 0.164 1.11 -0.318 -1.66 -0.029 -0.22Months since last employment1 -1.007 -9.47 -0.316 -2.22 -0.377 -1.28 0.086 0.562-3 -0.336 -4.09 -0.034 -0.31 -0.126 -0.47 0.418 2.6810-12 0.233 2.17 -0.134 -0.95 0.150 0.62 0.682 4.0613-24 -0.464 -6.88 -0.968 -9.39 1.035 6.39 1.053 8.80>24 -1.601 -15.72 -1.684 -14.64 1.564 8.83 0.982 8.69never worked -1.357 -12.39 -1.223 -7.95 -0.131 -0.43 0.006 0.03Household circumstancesMarried 0.064 0.92 0.085 0.91 0.077 0.65 0.173 1.93No. of children -0.003 -0.08 -0.030 -0.67 0.054 1.02 -0.020 -0.47No. of employed -0.033 -1.03 0.000 0.01 -0.175 -2.67 -0.160 -3.29No. of pensioners -0.067 -1.59 -0.021 -0.38 -0.010 -0.13 -0.084 -1.55No. of other persons -0.031 -0.92 -0.127 -2.47 -0.018 -0.34 -0.033 -0.740-3 years old child (dummy) -0.072 -0.84 0.112 0.92 0.006 0.05 0.043 0.384-6 years old child (dummy) 0.087 0.98 0.162 1.44 -0.110 -0.75 -0.068 -0.62RegionBudapest -0.411 -4.46 -0.324 -2.32 -0.777 -2.89 -0.246 -1.72High unemployment region 0.125 1.99 -0.376 -4.43 0.297 3.19 0.644 8.40Low unemployment region 0.061 0.69 -0.232 -1.84 -0.832 -2.82 -0.042 -0.31Constant 0.823 8.14 0.019 0.13 -2.082 -10.27 -1.126 -7.37Log-likelihoodPseudo R2

No. of observations 3239

-878.85839 -527.57093 -864.793040.2319 0.2619 0.27912049 3239 2651

Probability of UI receipt Probability of UA receipt

-1785.28550.1814

1993 1997 1993 1997

25

(Table 2 continued)

b) Women

Coef. z Coef. z Coef. z Coef. zEducational levelincomplete primary 0.048 0.30 -0.783 -2.14 0.241 1.11 0.241 1.11vocational 0.094 1.08 0.243 2.10 0.014 0.09 0.014 0.09general secondary 0.020 0.19 0.236 1.72 0.228 1.28 0.228 1.28vocational secondary -0.170 -1.66 0.321 2.41 -0.082 -0.40 -0.082 -0.40higher -0.352 -1.83 0.554 2.35 -0.072 -0.19 -0.072 -0.19Age-group (years)-19 -0.787 -5.26 -0.320 -1.50 -0.090 -0.31 -0.090 -0.3120-29 -0.219 -2.29 0.040 0.31 0.010 0.06 0.010 0.0640-49 0.043 0.45 0.161 1.23 -0.207 -1.24 -0.207 -1.2450-59 -0.082 -0.50 0.107 0.45 -0.624 -1.65 -0.624 -1.65Months since last employment1 -1.002 -6.38 -0.159 -0.80 0.343 0.67 0.343 0.672-3 -0.490 -3.72 -0.009 -0.06 0.031 0.06 0.031 0.0610-12 0.077 0.52 0.050 0.26 0.371 0.83 0.371 0.8313-24 0.214 1.31 -0.010 -0.05 0.185 0.36 0.185 0.36>24 -0.482 -4.38 -1.015 -6.20 1.312 3.64 1.312 3.64never worked -1.438 -11.26 -1.423 -9.05 1.548 4.20 1.548 4.20Household circumstances -1.599 -10.45 -1.326 -6.30 0.429 1.00 0.429 1.00MarriedNo. of children 0.157 1.88 0.017 0.15 -0.143 -0.98 -0.143 -0.98No. of employed -0.070 -1.61 -0.001 -0.02 -0.043 -0.58 -0.043 -0.58No. of pensioners 0.101 2.12 0.135 2.30 -0.214 -2.33 -0.214 -2.33No. of other persons 0.015 0.25 0.014 0.18 -0.015 -0.13 -0.015 -0.130-3 years old child (dummy) -0.058 -1.22 -0.088 -1.29 0.055 0.66 0.055 0.664-6 years old child (dummy) -0.353 -2.56 0.419 2.16 -0.394 -1.41 -0.394 -1.41Region 0.554 5.64 0.605 4.54 -0.339 -1.87 -0.339 -1.87Budapest -0.535 -4.95 -0.194 -1.35 -0.396 -1.65 -0.396 -1.65High unemployment region 0.031 0.37 -0.258 -2.14 -0.014 -0.10 -0.014 -0.10Low unemployment region -0.144 -1.12 0.117 0.75 0.059 0.24 0.059 0.24Constant 0.873 5.84 -0.296 -1.53 -2.274 -5.70 -2.274 -5.70Log-likelihoodPseudo R2

No. of observations

Probability of UI receipt Probability of UA receipt1993 1997 1993 1997

-996.96238 -542.32972 -283.20324 -283.203240.2507 0.2279 0.1815 0.18151943 1228 1943 1943

26

Table 3: Actual and Predicted Benefit Coverage among ILO/OECDUnemployed in Hungary

a) Unemployment Insurance

Year

actual fitted actual fitted1992 61.4 59.6 58.3 57.61993 59.9 59.9 56.5 56.51994 39.3 45.3 39.9 44.81995 26.6 35.5 28.5 32.31996 22.0 30.6 26.1 31.71997 24.6 31.1 25.7 30.3

The probability of UI receipt (ILO unemployed)

WomenMen

b) Unemployment Assistance

Year

actual fitted actual fitted1993 5.8 5.8 4.3 4.31994 16.8 13.2 13.3 12.31995 24.5 19.0 19.9 18.41996 25.1 19.6 20.0 18.11997 24.5 22.7 23.3 23.6

The probability of UA receipt (ILO unemployed)

Men Women

Note: The fitted figures are the means of the predicted values from probit modelsestimated for each year and then applied to the 1993 sample.

27

Table 4: Probit estimates of active search for 1997 in Hungary

Coef. z Coef. z Coef. z Coef. zEducational levelincomplete primary -0.049 -0.18 -0.316 -1.81 -0.084 -0.19 -0.053 -0.28vocational 0.334 3.05 0.241 2.41 0.269 1.89 0.160 1.14general secondary 0.837 3.92 0.545 2.05 0.076 0.46 0.160 0.82vocational secondary 0.305 1.82 0.589 2.85 0.363 2.11 0.277 1.36higher 0.172 0.48 -0.403 -0.78 1.171 3.28 0.140 0.28Age-group (years)-19 -0.023 -0.08 0.576 1.19 -0.135 -0.37 0.148 0.3520-29 0.164 1.15 0.095 0.75 -0.185 -1.15 0.066 0.4340-49 0.098 0.71 -0.059 -0.50 -0.076 -0.46 0.243 1.7050-59 -0.610 -3.39 -0.166 -1.05 -0.946 -3.37 0.022 0.09Months since last employment1 -0.395 -2.22 0.001 0.00 -0.075 -0.33 -0.473 -0.672-3 -0.323 -2.28 0.270 1.05 0.050 0.27 -0.011 -0.047-9 0.041 0.24 0.215 0.82 -0.245 -1.18 -0.242 -0.7410-12 -0.123 -0.68 0.720 2.63 -0.007 -0.03 0.321 0.9913-24 -0.238 -1.42 0.409 1.94 -0.187 -0.90 0.111 0.47>24 -0.358 -1.69 0.282 1.37 -0.409 -1.98 -0.029 -0.13never worked -0.483 -1.65 0.258 0.75 -0.144 -0.40 -0.274 -0.75Household circumstancesMarried -0.093 -0.73 0.194 1.71 -0.175 -1.28 0.039 0.29No. of children -0.015 -0.24 -0.122 -2.27 -0.022 -0.26 -0.026 -0.41No. of employed -0.014 -0.24 0.102 1.48 -0.009 -0.14 -0.183 -2.14No. of pensioners -0.153 -2.00 -0.039 -0.55 0.033 0.33 -0.191 -2.02No. of other persons -0.024 -0.33 0.007 0.13 -0.125 -1.34 0.007 0.100-3 years old child (dummy) 0.036 0.22 -0.001 0.00 -0.090 -0.40 -0.418 -1.814-6 years old child (dummy) -0.039 -0.27 0.170 1.20 0.114 0.70 0.033 0.21RegionBudapest 0.838 3.34 0.820 2.86 0.553 2.64 0.357 1.17High unemployment region -0.128 -1.13 0.112 1.17 -0.385 -2.72 -0.106 -0.89Low unemployment region 0.305 1.67 0.114 0.57 0.284 1.61 -0.186 -0.67Constant 0.195 1.00 -0.574 -2.38 0.098 0.40 -0.138 -0.51Log-likelihoodPseudo R2

No. of observations

-370.81220.0911

586

-384.70110.0379

596

Men WomenUI recipients UA recipients UI recipients UA recipients

798 883

-504.967030.0833

-579.071220.0537

28

Figure 1: ILO/OECD Unemployment Rate and Share of Long-TermUnemployment

0

5

10

15

20

25

Cze

ch R

ep.

Hun

gary

Pola

nd

Slov

akia

Slov

enia

Spai

n

UK

Une

mpl

oym

ent r

ate

(%)

0

10

20

30

40

50

60

Shar

e of

long

-term

une

mpl

oym

ent (

%)

Unemployment rate Share of unemployment that is long-term (>1year)

29

Figure 2: Targeting of Unemployment Benefit in Different Countries

All working age population

Czech

Hungary

SloveniaUK

Spain

Poland

Slovakia

0.0

50.0

100.0

0 50 100

% of benefit recipients who are ILO unemployed

% o

f ILO

une

mpl

oyed

who

get

ben

efit

MenWomen

30

Figure 3: Targeting Unemployment Benefit in Different Countries: those whohave worked less than 2 years ago

Last worked < 2 years

Slovakia

Czech R.

Hungary

Slovenia Poland

UK

Spain

0

50

100

0 50 100

% of benefit recipients who are ILO unemployed

% o

f ILO

une

mpl

oyed

who

get

ben

efit

MenWomen

31

Figure 4: Active Search and Targeting Unemployment Benefit in DifferentCountries: those who have worked less than 2 years ago

Last worked < 2 years

Slovakia

Hungary

Slovenia Poland

UK

Spain

0

50

100

0 50 100

% of benefit recipients who are active seekers

% o

f act

ive

seek

ers

who

get

ben

efit

MenWomen

32

Figure 5: Unemployment Benefit by time since last worked: ILO unemployed

Unemployment Benefit by time since last worked: ILO unemployed, men

0

10

20

30

40

50

60

70

80

90

100

Czech Rep. Hungary Poland Slovakia Slovenia Spain UK

<3 4-67-1212-24

Unemployment Benefit by time since last worked: ILO unemployed, women

0

10

20

30

40

50

60

70

80

90

100

Czech Rep. Hungary Poland Slovakia Slovenia Spain UK

<3 4-67-1212-24

33

Figure 6: Targeting of Unemployment Benefit in Hungary, 1992-1997

a) All search

45

50

55

60

65

70

45 50 55 60 65 70 75

% of benefit recipients who are ILO unemployed

% o

f ILO

une

mpl

oyed

with

ben

efit

MenWomen

1992

1997

b) Active Search

40

45

50

55

60

65

35 40 45 50 55

% of benefit recipients who search actively

% o

f act

ive

sear

hers

with

ben

efit

MenWomen

1993

1997

34

Figure 7: Changing Coverage of UI and UA of the ILO/OECD Unemployed inHungary

0

10

20

30

40

50

60

70

1992 1993 1994 1995 1996 19970

5

10

15

20

25

30

35

40

45

50

UI UA % of long-term U